Surface-wave dispersion spectrum inversion method applied to Love and Rayleigh waves recorded by distributed acoustic sensing

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. EN1-EN12 ◽  
Author(s):  
Zhenghong Song ◽  
Xiangfang Zeng ◽  
Clifford H. Thurber
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Love Wave ◽  
Wave Dispersion ◽  
Dynamic Strain ◽  
Inversion Method ◽  
Data Sets ◽  
Surface Wave Dispersion ◽  
Acoustic Sensing ◽  
Distributed Acoustic Sensing

Recently, distributed acoustic sensing (DAS) has been applied to shallow seismic structure imaging providing dense spatial sampling at a relatively low cost. DAS on a standard straight fiber-optic cable mostly records axial dynamic strain, which makes it difficult to separate the Rayleigh and Love wavefields. As a result, the mixed Rayleigh and Love wave signals cannot be used in the conventional surface-wave dispersion inversion method. Therefore, it is often ensured that the source and the cable are in the same line and only Rayleigh wave dispersion is used, which limits the constraints on structure and model resolution. We have inverted surface-wave dispersion spectra instead of dispersion curves. This inversion method can use mixed Rayleigh and Love waves recorded when the source and receiver array are not aligned. The multiple-channel records are transformed to the frequency domain, and a slant stack method is used to construct the dispersion spectra. The genetic algorithm method is used to obtain an optimal S-wave velocity model that minimizes the difference between theoretical and observed dispersion spectra. A series of synthetic tests are conducted to validate our method. The results suggest that our method not only improves the flexibility of the acquisition system design, but the Love wave data also provide additional constraints on the structure. Our method is applied to the active source and ambient noise data sets acquired at a geothermal site and provides consistent results for different data sets and acquisition geometries. The sensitivity of the dispersion spectra to layer thickness, density, and P-wave velocity is also discussed. With our method, the amount of usable data can be increased, helping deliver better subsurface images.

scholarly journals Skeletonized inversion of surface wave: Active source versus controlled noise comparison

2016 ◽  
Vol 4 (3) ◽  
pp. SH11-SH19 ◽  
Author(s):  
Jing Li ◽  
Sherif Hanafy
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Wave Dispersion ◽  
Dispersion Analysis ◽  
Inversion Method ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Velocity Models ◽  
Western Side ◽  
Geologic Setting

We have developed a skeletonized inversion method that inverts the S-wave velocity distribution from surface-wave dispersion curves. Instead of attempting to fit every wiggle in the surface waves with predicted data, it only inverts the picked dispersion curve, thereby mitigating the problem of getting stuck in a local minimum. We have applied this method to a synthetic model and seismic field data from Qademah fault, located at the western side of Saudi Arabia. For comparison, we have performed dispersion analysis for an active and controlled noise source seismic data that had some receivers in common with the passive array. The active and passive data show good agreement in the dispersive characteristics. Our results demonstrated that skeletonized inversion can obtain reliable 1D and 2D S-wave velocity models for our geologic setting. A limitation is that we need to build layered initial model to calculate the Jacobian matrix, which is time consuming.

scholarly journals Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion

2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Body Wave ◽  
Source Parameters ◽  
Wave Dispersion ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

P- and S-wave velocity models from surface wave dispersion curves data transform

2017 ◽  
Author(s):  
Valentina Socco ◽  
Farbod Khosro Anjom ◽  
Cesare Comina ◽  
Daniela Teodor
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Velocity Models

Joint inversion of two-dimensional magnetotelluric and surface wave dispersion data with cross-gradient constraints

2020 ◽  
Vol 221 (2) ◽  
pp. 938-950
Author(s):  
Pingping Wu ◽  
Handong Tan ◽  
Changhong Lin ◽  
Miao Peng ◽  
Huan Ma ◽  
...  
Keyword(s):  
Surface Wave ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Wave Dispersion ◽  
Uniqueness Problem ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Surface Wave Dispersion ◽  
Gradient Constraints ◽  
Dispersion Data

SUMMARY Multiphysics imaging for data inversion is of growing importance in many branches of science and engineering. Cross-gradient constraint has been considered as a feasible way to reduce the non-uniqueness problem inherent in inversion process by finding geometrically consistent images from multigeophysical data. Based on OCCAM inversion algorithm, a direct inversion method of 2-D profile velocity structure with surface wave dispersion data is proposed. Then we jointly invert the profiles of magnetotelluric and surface wave dispersion data with cross-gradient constraints. Three synthetic models, including block homogeneous or heterogeneous models with consistent or inconsistent discontinuities in velocity and resistivity, are presented to gauge the performance of the joint inversion scheme. We find that owning to the complementary advantages of the two geophysical data sets, the models recovered with structure coupling constraints exhibit higher resolution in the classification of complex geologic units and settle some imaging problems caused by the separate inversion methods. Finally, a realistic velocity model from the NE Tibetan Plateau and its corresponding resistivity model calculated by empirical law are used to test the effectiveness of the joint inversion scheme in the real geological environment.

Multi-Window Weighted Stacking of Surface-Wave Dispersion

Geophysics ◽  
2020 ◽  
pp. 1-53
Author(s):  
Sylvain Pasquet ◽  
Wei Wang ◽  
Po Chen ◽  
Brady A. Flinchum
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Real Data ◽  
Wave Dispersion ◽  
Lateral Resolution ◽  
Data Sets ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Shallow Subsurface ◽  
Depth Of Investigation

Surface wave (SW) methods are classically used to characterize shear (S-) wave velocities ( VS) of the shallow subsurface through the inversion of dispersion curves. When targeting 2D shallow structures with sharp lateral heterogeneity, windowing and stacking techniques can be implemented to provide a better description of VS lateral variations. These techniques, however, suffer from the trade-off between lateral resolution and depth of investigation, well-known when using multichannel analysis of surface waves (MASW). We propose a novel methodology aimed at enhancing both lateral resolution and depth of investigation of MASW results through the use of multi-window weighted stacking of surface waves (MW-WSSW). MW-WSSW consists in stacking dispersion images obtained from data segments of different sizes, with a wavelength-based weight that depends on the aperture of the data selection window. In that sense, MW-WSSW provides additional weight to short wavelengths in smaller windows so as to better inform shallow parts of the subsurface, and vice versa for deeper velocities. Using multiple windows improves the depth of investigation, while applying wavelength-based weights enhances shallow lateral resolution. MW-WSSW was implemented within the open-source package SWIP, and applied to the processing of synthetic and real data sets. In both cases we compared it with standard windowing and stacking procedures that are already implemented in SWIP. MW-WSSW provided convincing results with optimized lateral extent, improved shallow resolution, and increased depth of investigation.

scholarly journals Time-lapse monitoring of climate effects on earthworks using surface waves

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. EN1-EN15 ◽  
Author(s):  
Paolo Bergamo ◽  
Ben Dashwood ◽  
Sebastian Uhlemann ◽  
Russell Swift ◽  
Jonathan E. Chambers ◽  
...  
Keyword(s):  
Surface Wave ◽  
Transportation Network ◽  
Wave Dispersion ◽  
Time Lapse ◽  
Spatial And Temporal Variability ◽  
Data Sets ◽  
Initial Model ◽  
Surface Wave Dispersion ◽  
Wave Data ◽  
Surface Wave Data

The UK’s transportation network is supported by critical geotechnical assets (cuttings/embankments/dams) that require sustainable, cost-effective management, while maintaining an appropriate service level to meet social, economic, and environmental needs. Recent effects of extreme weather on these geotechnical assets have highlighted their vulnerability to climate variations. We have assessed the potential of surface wave data to portray the climate-related variations in mechanical properties of a clay-filled railway embankment. Seismic data were acquired bimonthly from July 2013 to November 2014 along the crest of a heritage railway embankment in southwest England. For each acquisition, the collected data were first processed to obtain a set of Rayleigh-wave dispersion and attenuation curves, referenced to the same spatial locations. These data were then analyzed to identify a coherent trend in their spatial and temporal variability. The relevance of the observed temporal variations was also verified with respect to the experimental data uncertainties. Finally, the surface wave dispersion data sets were inverted to reconstruct a time-lapse model of S-wave velocity for the embankment structure, using a least-squares laterally constrained inversion scheme. A key point of the inversion process was constituted by the estimation of a suitable initial model and the selection of adequate levels of spatial regularization. The initial model and the strength of spatial smoothing were then kept constant throughout the processing of all available data sets to ensure homogeneity of the procedure and comparability among the obtained [Formula: see text] sections. A continuous and coherent temporal pattern of surface wave data, and consequently of the reconstructed [Formula: see text] models, was identified. This pattern is related to the seasonal distribution of precipitation and soil water content measured on site.

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